Apps News Privacy

Android Developers Can Use Google AdMob And Comply With GDPR

Google is asking app developers who publish apps on its play store to obtain consent for data use and for ad personalisation through its AdMob platform. 

Due to the coming GDPR legislation which comes into effect on the 25th of May. Any business based in the EU will need to gain opt-in consent to collect or use any of their user’s personal data. 

This news places the responsibility of obtaining consent for Google’s services that are running in the background (such as AdMob targeting) on the shoulders of the publishers. Android developers are expecting to see some kind of software kit to help them obtain and manage this consent. As of now, and up until the new legislation kicks in, Google has not announced any SDK or toolkit that could solve this headache for Android developers. 

Many developers lacking the time or manpower to create such a kit are weighing up their options ahead of the legislation. Some have even hinted at switching of these third-party services for users int eh EU. 


The problem

Breaches of the legislation carry with it the threat of huge fines. User consent has always been an issue for app publishers. Creating a solution for obtaining consent and then managing this consent is no easy feat. Integrating this consent with third-party integrations (such as advertising solutions) adds another layer of complexity. 

For Android developers, the problem is a little more pressing as AdMob revenue is what keeps them afloat. Developers may find themselves stuck between a rock and hard place – turning off AdMob would instantly create a big hole in their revenue. However, keeping it on and exposing themselves to potentially destructive fines doesn’t seem like a viable option either. 


So what’s the solution?

With the right toolkit developers wouldn’t have to change their business model too much. Letting users opt out of ads might lose some revenue but it’s a necessary step to take to comply with the changing mood around privacy and transparency. 

Controlling user data in a responsible way makes sense because it builds trust and in the long term it will be beneficial for developers. 

Luckily for developers, there’s a toolkit that is addressing this problem. Via a dedicated SDK app, publishers can continue to use third-party ad integrations, such as AdMob. The toolkit obtains and manages user consent to help developers comply with regulations such as GDPR.

As well as this the toolkit will sync user consent across devices. All consent preferences are stored in a secure audit trail so that developers can call on consent history of their users. The audit trail also contains information on consent preferences that have been replayed to third parties. In the AdMob example when a user opts out of personalised ads in their app the consent SDK will relay this to Google. The audit will register this along with a timestamp and other relevant details.

The SDK provides this functionality for first-party app features as well as third-party integrations. It’s a comprehensive toolkit to take control of your user consent. 

This toolkit doesn’t need to only apply for Admb or even android. A wider conversation about the role of consent in mobile applications needs to be had. Developers should look at how consent is obtained, managed and communicated to third parties. 

Complying with GDPR is a shortsighted approach. Developers need to put their users first and think about how they can put these users back in control of their data.

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App GDPR Toolkit – How Developers Can Prepare Apps for GDPR

When GDPR is concerned, developers can’t afford to overlook app user privacy, consent and opt-in preferences. Here’s five tips that will get you compliant.

It’s a huge problem for app publishers. How can you comply with intimidating privacy legislation and maximise the number of users that are opted into your app services?

By some estimates over 50% of current apps are not compliant with the new GDPR legislation.

That’s because apps have multiple third parties and SDKs integrated. Many of these are asking for data on users.

It’s difficult for publishers to keep track of this. But it’s now the law to be in control of this data.

It shouldn’t have to be this difficult to comply with privacy regulation. And it shouldn’t be hard for your users to opt-in and out of individual preferences.

Lucky we think we’ve found a solution for developers to manage, sync and audit consent in their suite of mobile apps. 


Asking for consent and getting your users to opt-in

Complying with privacy legislation isn’t the most straightforward process.

And how do you make sure that you don’t spook your users into opting out of all services? User opt-in is important to obtain as it can be a great tool in which to drive engagement and retention, not to mention monetization.

You need to ask user to opt-in at the right time. And you need to be clear that they are in control. We tried to solve this problem by designing our consent toolkit to help developers obtain and manage user consent.

Many apps get opt-in timing wrong. Don’t ask for all permissions the first time that the user opens the app. Explaining the value that users will get in return for opting in for certain permission will mean that the user is better educated about what their data is being used for.

Make sure that your opt-in process is clear and be upfront with your users.


Manage user opt-out requests respectfully

Under new legislation is just as important to ensure that users can opt out as it is to obtain consent properly in the first place. To do this publishers must have a system in place that can allow their users to opt out of some or all of the permissions that they have previously opted in for.

This was one of the fundamentals that shaped the way our consent module works. We wanted our toolkit to make it as easy for users to opt-out and it is to opt-in. This needs to be done in a way that doesn’t just put the user in control of their data but allows them to choose which kinds of data is used by publishers.


Make sure you can manage consent across devices

Consent and user opt-in management are difficult enough to get right as it is. But this can be made nigh on impossible when you consider the fact that app users are constantly deleting apps and changing devices. 

Syncing user settings are important because if a user has revoked a permission on one device then to continue to use this could be a breach of privacy regulation. Also, if a user requests that all their data be deleted, this is difficult to do unless you can identify everywhere that the user has given access to data.

That’s one of the problems that the consent toolkit was built to solve. By using a series of unique identifiers it’s possible for developers using the toolkit to sync consent preferences. In this way, the consent toolkit manages a users consent and opt-in/opt-out preferences whenever they interact with an app or service.

This is especially useful when a user requests their data be deleted (or in GDPR terms – right to be forgotten). Having a toolkit that syncs across devices allows publishers to remove this data and stop collecting it wherever the user is seen in the future.

Sometimes it’s a messy infrastructure. What happens if a user updates consent preferences in one app, but uses other apps from you? Make sure you can sync this preference across your real-estate.


Integrate user consent with third parties

Apps rarely run in isolation. You might have third party services, or other SDKs that have access to our user’s data.

These need to be kept in sync with the user’s opt-in preferences. If your user says no to communication, this needs to be updated with third-party advertisers for example.

At Tamoco, our consent module allows apps to instantly update third parties with new user preferences. If a user asks for all of their historical data to be deleted this information needs to be relayed to third parties.

The consent SDK communicates this to third parties automatically when a user’s preferences are updated.

Information of this is then secured in a secure audit trail. The consent module will automatically ask third parties to confirm that they have received these requests for changes in a users preferences. When this is (or is not) received this is saved in the audit trail, along with timestamps and relevant information.

This means that developers can ensure that their users’ opt-in preferences are respected in third-party integrations. It’s important to be able to follow an audit trail to prove that this information was relayed to third-party partners and integrations such as SDKs.


Make sure you have a secure audit trail

With the correct procedure in place, developers don’t need to worry about manually managing consent. But what happens if you ever need to prove that your app has protected user data.

App developers need a way of storing the history of user consent. It should be easy for developers to prove that historical consent has been obtained.

In our consent toolkit we provide developers with an audit trail to do just this. Everytime a user changes their consent preferences then the SDK automatically records this with time stamp.

This ensures that app publishers are always covered. This information is easily viewed and provided for reference. Third-party consent is also stored in the audit trail. All requests for opt-out are sent to third parties and the record of this is then stored in the audit.

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Marketing & Advertising

Best Guide To Location-Based Marketing & Advertising 2021 + Examples

So as a marketer you want to know how location-based marketing can help you to reach your marketing goals?

It’s time to take a serious look at location. Big data is tearing up the rulebook in a number of different industries. This trend continues with data-driven marketing becoming the new normal. Mobile has changed many things, but it’s having a huge effect on the way that markers are using data to reach their goals.

The missing link in this equation is location data. The rise in mobile adoption has provided a much better and accurate understanding of how audiences behave in the offline world. This location data is allowing marketers to do incredible things, based on cold hard evidence.

We’re going to look at some examples of location-based marketing. Get ready to see how you can use location-based marketing to create effective campaigns. Learn how to use location data to provide powerful insights and measure attribution with precision.


What is location-based marketing and big data marketing?

Location data can be seen as a branch of big data. When the term big data is used people generally think first about quantity. Whilst this probably has to do with the reason that the terms exist, big data isn’t really about quantity.

We think that data is big in the sense that the impact is big. We think of location data as big because of it’s quality in both application and insight.

With that in mind, we can define big data as the collation of data from multiple sources. To inform better decision making, powerful targeting, and improved attribution.

Location data is big data that uses information about a person or group of people’s movement or behavior. This is used to understand wider trends and patterns. Location-based advertising and marketing use this data to fine-tune marketing efforts. But it is also used to generate better engagement and get valuable insights into customer behavior.


How can my business use location data and location-based mobile marketing

For marketers, it has sometimes been difficult to understand the benefits of location data. Especially whilst trying to get around the technical side of how it works. In the beginning, many companies had inaccurate data sets. But now the science behind location data has advanced greatly. This enables marketers by providing quick and reliable results. All by incorporating location data into their marketing strategy.

These uses are now much more accessible and easily combined with existing marketing efforts. Plug and play location-based marketing is now available. With this in mind let’s look at some of the key marketing practices that benefit from location data.


Location-based segmentation

Audience segmentation is a key challenge for any marketer. In order to optimize marketing dollars, it’s important to make sure that you are reaching the right people. It can sometimes be difficult to get this right, and often involves a lot of hypothesizing and testing as well as optimization.

Location can help to build powerful audience segments as it’s a key indicator of intent. For example, let’s say you make the active decision to walk into a specific location in a shop. It is then likely, at some level, for you to be interested in some of the products in that location.

Location can also be used to build historical audiences based on location history. This means that you might target a group of people who are health nuts. You’ll have many ways of doing this currently. But location adds something that isn’t possible through traditional targeting options.

You can build an audience of people who visit gyms twice a month and have been to a dedicated health and fitness store in the past three months. If you have a strong idea of the type of target audience your product sits well with then location is a powerful tool for identifying custom segments.

An important point to make is that these audiences can then be used in the way that best suits your needs as a marketer. You can target them through social media ads or send the data straight to a trading desk, DSP, DMP or other ad network. You can use it to overlay custom audiences to understand overlap. You can even use location to see the accuracy of your existing audiences and targeting.

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Improve your audience segmentation

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Location-based targeting

Location tech is valuable for marketers because of the instant nature of data. Real-time insights allow targeting to occur in the moment, especially on mobile.

Location-based targeting is a powerful tool in any marketers arsenal. For targeting when users are in the right frame of mind for conversion, location is effective in driving engagement.

But location-based targeting is great for physical businesses with products in the real world. The ability to target audiences when they are either geographically close or in the right moment, location can be very effective in driving footfall or driving brand engagement.

An example is a location-based campaign that targets users when they are close to a physical store of venue. When the user enters a pre-defined location they are given a message that informs them of the CTA that is nearby.

This can be in the form on a push notification via a third party app on their phone. But it’s also possible for marketers to feed this real-time data into existing media buying tools that can deliver ads via other programmatic media. As long as this is real-time the audience is still in the relevant moment and therefore effective.

These kinds of campaigns get much higher engagement and conversion rates. Marketers can use location data to ensure that their real-time targeting is effective. With location, you’ll also get insight from these kinds of campaigns. These insights can help you to optimize your entire marketing department.


Location-based attribution

This is an area where location data is offering unique insights for marketers. The ability to measure the effect of advertising in the offline world is a relatively new concept. Especially at a level that rivals the detailed insights that are readily available in the digital realm.

Location-based advertising attribution is helping marketers to understand the complete effect of their efforts in the real world. Offline attribution is effective in a number of ways:


Measuring OOH

OOH, real-word adverts are big business for marketers. But there’s always been a problem – how do you measure the results? It’s difficult to attribute store visits or purchases to OOH. It’s also difficult to understand exactly how many people are exposed to advertising in the first place.

Location data makes these insights accessible. By listening to areas around OOH it’s possible to measure how many people have passed or remained close to the OOH advert. From this data, you can create insights on how many people have been ‘exposed’ to the OOH ad. Of course, this isn’t perfect as there’s no guarantee that everyone walking past saw or understood the message.

But location data makes it possible for marketers to then measure how many of these people perform the desired goal. This may be that they visit a retail store associated with the ad. This is an effective tool for marketers to be able to measure, test and optimize OOH advertising.


Measuring experiential or other offline advertising

Of course, this tech can be used to measure other forms of advertising. Take experiential, for example. Usually, these campaigns end with the consumer leaving with a sample of some kind. But attribution doesn’t come easy and many campaigns end with the basic insights. These are usually how many people visited the experiential stand, or how many samples were handed out.

But location data enables marketers to then say, with great precision, this many people engaged with our experiential stand. You can then identify the percentage of these people that visited the store within a certain period of time.

These insights are invaluable. They provide marketers with the opportunity to get digital insights on traditional offline marketing campaigns.


Measure the effect of digital advertising on offline goals

Location data is a powerful tool to associate online digital advertising to offline conversions.

For example, if you have a Facebook campaign you will have an idea of how many people saw your ad and even how many of these clicked your ad. But if your conversion is in the offline world, ie visiting a physical store, then this is where your campaign traditionally ends.

Sure there are some things you can do to pick up customers on the other side, like offer codes or loyalty schemes. But none of these will offer the same precision or reach as location data-driven marketing attribution.

Using location data marketers can understand the offline effects of digital advertising.

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Do attribution better, with location

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Business intelligence and business analytics

For marketers, getting those insights on a micro and macro level are crucial when creating your strategy. In terms of insight, data is the new normal. You want to base your marketing decisions on data that is accurate and instant.

Understanding the customer is critical to any marketing department. Location intelligence is a powerful tool in the area of customer analysis. Try enriching customer data with demographic and anonymous lifestyle information. This allows marketers to create more effective databases and be better placed to predict where best to spend the marketing costs.

For brands with a physical venue or store location data can provide powerful insights into business performance. Understanding footfall and trends can help to inform on the ground business strategy. For example, retail location data can also help strategize how best to compliment physical retail stores with digital advertising.

Combine this with the ability to see data on competitors and other physical location and you have a powerful toolkit that marketers can use to put data at the centre of their decision making.


Personalization with location

For marketers, a key goal is to try and personalize the relationship between brand/product and the consumer/user. Of course, this can be difficult as it’s not always easy to completely understand your audience. It’s even more difficult to personalize your communications based on this, especially on a one to one level.

But analytical intelligence can be one solution to the personalization problem. Identifying the location of a customer can help brands and marketers to customize their message so that it is personal.

This could be a simple as including terms like welcoming back in your messaging. Or you can create entirely different communication for customers that are in different locations or have demonstrated previous patterns of behavior.

Communicating with your customers in this way can help to build stronger relationships and increase brand loyalty. This allows you to communicate with the right customer when they are in the right place with the right message.


How does location-based marketing work?

Location data is sourced from mobile devices. Sensors are used to understand and pinpoint these devices. This process is anonymized so that the user’s personal details are kept private.

These sensors come in a variety of forms – from beacons to Wi-Fi to geofences. Using a combination of sensors allows for greater accuracy and better scalability of data.


What is good location data?

You’ll need to make sure that your data provider is doing two things:


Can validate the accuracy of the data they collect.

This means you’ll need to understand what types of location data there is. Some are more precise than others. Some are real-time and others are delayed.

Generally, a data provider that can explain to you their methodology and is transparent about their data sources is a good start. Look for sensor-driven data sources such as beacons, GPS or wi-fi. First party data sources are much better than third-party, where the provider cannot validate the accuracy.


Can ensure that data is collected in a safe and secure way

Does your data provider have the correct opt-in procedures? Do they comply with current data collection legislation? These are all important questions that any good location-based marketing company will be happy to explain to you.

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Add location to your marketing today

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What is location-based marketing?

Location-based marketing, also named geolocation marketing, is a form of mobile advertising that is highly personalized based on where the consumer is or has been.

How does location based marketing work?

Location based marketing works by using real-time device location to build detailed prolifes of how consumers move and behave in the real world.

Is an app needed to engage with location-based marketing?

Not at all, even brands without a dedicated mobile app can start using location based marketing to engage with consumers.


GDPR & CCPA For Apps – Tips For Privacy Compliant Apps

Let’s look at GDPR, the CCPA and how you can make sure that your app is ready for the coming changes.

What’s the most important currency around? It’s data. It’s used to fuel everything from your personal virtual assistant to your social media feed. But let me tell you one thing about this data. It’s private, it needs to be safeguarded and soon, fellow app developers, it will be the law for you to ensure this.

Data is so omnipotent in our digital lives. Privacy regulation is set to make data handlers liable for how they collect, protect, store and remove this data. Some have predicted that up to 55% of apps aren’t ready for this change.

But you thought GDPR is only for email marketers. Wrong. Complying with privacy regulations is integral to running a successful mobile app business. As a mobile developer, under the new legislation, you will be responsible for all the personal data from your app.

That’s right – as of the 1st Jan 2020 responsibility will rest with you to ensure that you are in control of user data. But it doesn’t have to be all doom and gloom. The GDPR and CCPA are an opportunity for developers to create effective relationships with their users. It also means that you can offer up a great app experience at the same time.


But what is GDPR and CCPA?

GDPR stands for the General Data Protection Regulation and it came into effect on the 25th of May 2018. It is designed to protect data as it is collected and stored. It is also in place to ensure that the user is in control of their data. It seeks to allows the user to easily opt-out and remove their data when they so desire.

The CCPA is similar and will come into play on the 1st of Jan 2020 – the California Consumer Privacy Act is a bill meant to enhance privacy rights and consumer protection for residents of California, United States.

For apps, this means that a proper system for opt-in, data collection and data storage will need to be in place. As well as this the infrastructure to opt-out and be forgotten are essential to comply with the legislation.

There are some key principles to define when looking at the legislation from a developer’s perspective. We will help to explain these next and look at exactly what these principles mean for developers, as well as practical advice for app owners.

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Explicit consent

This is a key requirement for mobile apps. The legislation says that businesses must request and receive consent to collect use and move personal data. Further, this request must be made and given in clear intelligible and easily accessible way. It cannot be confusing. As well as this the user must be able to withdraw consent as quickly as they can give it.

This means that apps will need to communicate better with their users. They must clearly define the type of personal data they collect around users. Developers will need to explain why this data is collected and obtain clear consent to collect this information.

Practically this means that you may wish to ask for certain types of personal data at different points of the user experience. For example, it’s generally a better idea to ask users for data consent at a point where it is relevant to the action that the user is performing.

So don’t ask for every permission under the sun the first time your app is opened. It might be better to wait for the right moment to communicate these to the user.

This also gives you a better opportunity to communicate the value that the user will receive by opting-in for this type of data collection. It also means that you can clearly explain opt-out procedures as well (but more on that later).

For example, we help our partner apps to obtain consent for location permissions by providing a dialogue with the user at the right moment. This could be when the user is looking for nearby venues or searching for local deals.

By clearly explaining to the user at this moment it allows the user to come to an informed decision on how they want to share their personal data with the app. This complies with the ‘explicit consent’ as defined in the GDPR legislation.

Find out more about asking for consent by speaking to our app team.


The right to be forgotten

One of the keys focuses of the legislation is the right to be forgotten. This means that app developers will need to create a system of opting-out that allows users to be in control of the data collected through the app.

As previously mentioned this should be as simple for the user as opting-in. Your app users should be able to request that their entire data history is deleted and removed from all records. This includes third parties (yes that means every SDK that you have used in your app that uses personal data).

For developers, this means designing user control into the app so that the user can perform these actions when desired. Apps must be able to process and act upon these user requests and then ensure that all personal data is removed.

This might be in the form of an option to contact you with questions about your data.

Or you can add a data section to your app settings page that allows your users to opt out of different types of data collection. You can also add the option to revoke all data collection.

The aim of GDPR in this area is the put the user in control of their data. If you can design your app to facilitate this control then your app will be compliant and your users will have a better experience when using your app.


Privacy by design

This section is all about the proper encryption and data handling procedures.

You might think that this is an obvious approach to take when designing a mobile app. Perhaps you have considered privacy at multiple points in the planning of your app. That’s great – the key points to remember is that GDPR makes this a legal requirement.

So from a project’s inception to every point in the lifecycle privacy and data protection will need to be front and centre. It’s about anticipating, managing and preventing privacy issues. And doing this before a single line of code has been written.

There are fundamentals that app developers will do well to follow once the legislation comes into force:

Privacy must be proactive, not reactive, it must also be preventative not remedial. This means that developers should be thinking about privacy from stage one of the design process all the way through to after the user’s app engagement has ended.

Define the kinds of data that your app will use in the design phase. Assess potential issues that may arise when using this data. Make sure that your app is designed to secure this data by default and has the correct opt-in processes before you do anything with this data.

When processing user data ensure that your systems are designed to secure the data. This might mean pseudonymization of data or even creating a completely secure way of processing personal data.

The basic idea here is that privacy and data control to become a key part of designing any new app feature. By taking this approach you create an app experience that is secure. It provides users with the controls to input personal information in the knowledge that it is secured and that they can have it removed at any time.


Consent module and Tamoco’s secure SDK

As mentioned one area where developers need to ensure compliance with GDPR is through the use of third-party SDKs. Many of this access and use user data, and often there is not explicit consent for this from the end user.

If you’ve been paying attention you’ll realise that this is a direct breach of GDPR. As a developer, you’ll need to balance the use of third-party SDKs with user privacy and consent. Partnering with SDKs that place user opt-in front and centre will be a sensible approach once GDPR comes into effect.

At Tamoco we help apps to comply with the new regulation whilst providing a powerful toolkit to boost app engagement and monetization. Our product allows apps to get valuable insights and analytics into their app audiences whilst ensuring GDPR compliance.

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Five Key Mobile App Statistics App Developers Should Know

Sure there’s yearly reports on everything from app usage to revenue. We welcome these and they can provide developers with vital information about the app economy. But often it can be difficult to understand how these trends will affect your app.

So we’ve tried to help. We’re going to look at five interesting stats based on data from the last year. Then we’re going to attempt to understand what these trends show, how it will affect monetizationengagement and other app metrics. We’ll also look at how developers can adopt their app strategy to suit these trends.


Last year app mobile device app downloads reached over 175 billion

This represents a 60% growth on 2015. Now that’s healthy, and there are a few reasons for this.

Firstly, more apps are free to use or try and more developers are finding this model attractive. For the consumer this means that apps are free to download. With the rise in subscription models and other post-download monetization options, this is greta news for publishers too.

The number of smart phones in circulation has increased, especially in emerging markets. Rapid mobile adoption shows that there is still huge potential for app growth.

Mobile devices now have much better storage options. Users previously had to manage device storage carefully. The lowest storage bracket on newer devices has increased and cheaper cloud options leave a lot more space on devices for apps that would have previously taken up too much space.

Finally, people are prioritising mobile to complete tasks that would have usually been difficult on a mobile device. Apps are now much more secure and user-friendly. This makes tasks like shopping or managing finances much easier.


What can developers learn from this?

You should think about making your app free to download and monetizing after the app experience. Users increasingly expect apps to be free.

Whilst it’s still important to keep the size of your app as low as possible, this isn’t as much of an obstacle as before. Instead users are looking for apps that help them to achieve tasks on their mobile. Positive user experience is important for users. They want to be able to do powerful things in a great app experience, without having to open their laptop.


Consumer spend exceeded $86 billion

When we look at the total spend by users the figures make for positive reading. This growth remains strong thanks to the increase in smartphone adoption in the developing world. The ability for publishers to capture more revenue from their users should not be overlooked.

In terms of the app store, app revenue is still higher in iOS than Google play. Worldwide gross app revenue reached $38.5bn from the app store in 2017 compared to $20.1bn from the Google play store.

This shows that Apple products do continue to attract, on average a user that is is more likely to part with cash via apps. However, both stores showed similar revenue growth levels of around 35%.

The consistent growth suggests that publishers are successful implementing monetization strategies. This is allowing them to generate more revenue per user. This may include subscriptions and freemium etc.

Developers will be happy to see that monetization in top markets maintained a steep growth – 70% in the US and 35% in the UK. But the real story of the last year in terms of app development is the scale of growth in developing markets.

The short story is this – the app economy is in a great place right now. Consumer spend has doubled in 2017. Publishers will need to look at their monetization strategy in developed markets. Here they will need to balance experience with monetization. As well as this they should be looking at new ways to monetize without choosing an advertising solution.


App store consumer spend in China grew by 270% in one year

App store spend is growing at a much faster rate in emerging economies.

China and emerging markets are fantastic examples of where developers should be looking in terms of app monetization. In the last year apps are becoming widely used in citizens’ daily lives. Much in the same way that apps have revolutionised other lifestyles, the same is happening in emerging markets. This is because more people are using mobile devices to perform daily tasks.

Rapid growth in downloads across other developing nations will provide even more opportunity for growth.

There now exists a lag between the number of downloads in these emerging markets and the equivalent revenue for app developers. The potential for monetization is huge. Publishers need to move to make sure they can tap into one of the biggest monetization opportunities out there.

Add to this that India and Brazil are areas where app usage is also increasing at at an alarming rate. India is now in second place globally in terms of number of app downloads. In these economies Android devices are more popular. This means that ensuring you can support both platforms could be the key to sustained growth.


What does this all mean for developers?

Firstly, we can still conclude that the average iOS user is worth more than an Android user in terms of monetization potential. But growth is steady across both OS.

The success of publishers monetizing after the point of purchase continues to drive revenue in developed markets. Subscription models and other models allow time for the publisher to educate and engage users on their apps value. This encourages better monetization. Ads are still a strong source of revenue for apps. But apps as a service are increasing in number and developers are getting good results from this monetization model.

In developed markets app discovery is becoming more difficult. But, the potential for revenue through monetization after the point of download is increasing.

Mobile apps are dramatically increasing in the developing world. The rapid number of new device adoption means a huge amount of new users. The value of these users is still low compared to developed markets. But, this still represents a huge opportunity for revenue growth.


Each mobile user spent 1.5 months in apps

It’s safe to say that users are spending more of their time in apps. And it’s also pretty certain that users are using more apps, on average. Last year users spent on average over 3 hours a day in mobile apps.

This presents far more opportunities for developers to create effective engagement strategies. Users want to complete more tasks on a mobile device and they love to be able to do this in apps.

Improving lifetime value and customer satisfaction is a crucial part of creating a successful app. Being able to engage apps leads to better monetization and more chance of increasing your user base quickly.

There are two things going on here. In developed markets, users are doing much more on their phones. But in emerging markets users have skipped the use of a desktop and see mobile as an effective way to complete certain tasks for the first time.

The time is now for developers to put experience centre of their app strategy. Their app solution should take advantage of the increased amount of tasks that users are doing on mobile. In some ways engagement is more important than downloads – if you can’t keep users in your app then you’ll churn users and very quickly have a worthless app. These figures show that users want positive experiences and the ability to complete their goals inside apps – developers should focus on delivering this.


The average smartphone user accessed around 40 apps per month

More tasks than ever are being completed on mobile.

You might think that all that time is being spent on the bigger apps. This is simply not the case. Users are looking to apps to perform a variety of tasks that can only be achieved by a single app. Users are looking for powerful apps in each category and they are choosing the ones with the best experience and best tools for the job in question.

Engagement is of course important for monetization. Keeping users engaged and happy is key to generating high revenue. That’s why these stats are promising for developers. If you can successfully implement a great engagement strategy you will be able to monetize effectively.

As app attention grows publishers will need to understand what this means for their app. This could mean focusing on UX and understanding how improving this will mean more engaged users. Or it could mean focusing on how push notifications can improve app retention.


What does this mean for developers?

As users spend more time in apps and use apps to solve problems and complete tasks developers will need to seize the opportunity and ensure that their app offers a seamless user experience.

Experience is key to successful monetization. Publishers that are looking to increase revenue, especially in emerging economies will need to focus on retaining their users.

The stats say that users are spending more time in apps, but they won’t just choose any old app to reach their goals. Apps still need to be powerful and they still need to have a great experience to attract and retain users.

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Grow your app with Tamoco

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How Big Data & Location Intelligence Is Changing The World

There’s no doubt that the explosive rise in the number of smartphones has changed the world as we know it. The increased number of sensors and connected devices has produced mountains of data. This is being used to transform the way that we live our lives.

IoT, location data, location intelligence, big data. Whatever your name for it, it’s hard to dispute the potential across a variety of industries

It’s now apparent that granular location data can provide unprecedented insight into the offline world. More businesses are realising the value of mobile location data and the impact it is having across the globe.

As we move away from unreliable data sets, sensor-driven accurate data sets are taking centre stage. This kind of accurate data has many applications. But I’d like to look at some that are having the greatest disruptive impact.

Business intelligence

The ability to notice trends by using data isn’t new. The ability to do this based on people’s activity in the offline world, in a close to real-time manner is.

Location intelligence reveals relationships between big data sets that often would be missed. It turns these insights into actionable business intelligence. Helping inform decisions, from the boardroom to the storefront.

From the small bar that is competing with huge chains of venues through to the small retailer competing with online mega corporations. These businesses are gaining valuable insights from using this kind of big data to inform their business strategy.

The truth is that mobile location data has now matured enough to solve many problems that both small business and enterprise face. Let’s look at a few:

Financial services – understanding footfall through big data sets is valuable for the financial sector. Mobile device data can help to forecast earnings and other KPIs before they are formally reported. This helps inform investment decisions.

Retail – big data can help both small and large retailers. Understanding store visits, as well as customer behaviour through mobile device data, is having many positive effects on the retail sector. These insights can help inform business decisions such as store layout, opening times, staffing and more.


Infrastructure and planning

We’ve all heard of the term smart city. We like to think that there’s more to it than just adding a few data points and putting the word smart in front of it. It is, in fact, more than that. We’re moving towards urban centres with huge populations and aspiring towards self-driving vehicles. Big data is the key to unlocking this truly smart future.

The rise in mobile device data has provided better opportunities to understand how cities work.  It’s helping to create systems and infrastructure that reflects this.

Combined with the increasing number of connected devices in cities, central planning authorities now have a set of tools that can inform decision making in many different areas.

Mobile location data is contributing to a better understanding of where demand for public infrastructure is greatest. For example, examining mobile device location data to understand the most cycled roads within a city. This information is precise and invaluable when planning where to implement new routes.

The same is true of traffic and congestion. In increasingly crowded and polluted megacities, it’s important to understand how traffic issues can be alleviated. Understanding traffic flow and where to build new road structures, or introduce new low emission zones is crucial to building the kind of smart city that can sustain current levels of population growth.

Big data is having a huge positive effect on this kind of planning. Thanks to the accuracy and uniqueness of mobile device data and location intelligence, it is changing how decisions are made in cities and towns around the world.

Marketing and advertising

Big data and marketing have always complimented each other. Marketers have always looked to use data sets to improve the efficiency and effect of ads. Using big data to create tailored and relevant audiences is not a new practice.

But mobile location data allows marketers and advertisers to connect digital advertising to how consumers behave when they are offline. Understanding how consumers move in the offline world is helping marketers to become more effective. It’s assisting marketers in providing more personal advertising to consumers.

Location intelligence is disrupting many stages of the consumer lifecycle. It’s bringing the analytical capabilities that have been available for the web to the real world.



Mobile device data is helping to build up complex pictures of how people move and behave. This helps advertisers to build complex customer profiles. Brands are finally understanding the places that their customers go and how they interact with the physical world around them.

This is far more effective than other methods of audience segmentation. This is because a person’s location is often a much greater sign of intent than when they are searching for something on a computer, or browsing on their phone whilst sat on the couch.

This allows marketers to identify exactly where consumers are on the buyer journey. Moreover, it allows them to do this with a greater level of detail.



One big breakthrough that big data has had on marketing and advertising is by increasing the ability personalise at scale.

Location data is allowing brands to be helpful and human by understanding the situation of the customer. The concept isn’t new, but the accuracy and increasing size of data sets in the space have allowed commutation to really get personal.

Location help provide promotions at the moment when the customer can actually redeem it. It allows the ‘customers also bought’ experience to reach the real world retail store. In this way, big data is providing digital solutions to offline problems. Location intelligence is tailoring brand communications to a person’s unique experience of the real world.


Customer experience

Big data has changed customer experience for the better. Location intelligence can help to automate way-finding, ordering, assistance and queue management. Understanding the physical location of a person has helped improve the guest experience across many sectors.

Stadiums, resorts, airports, transport hubs all stand to improve the experience of the people who spend time and money in these places. It might be location based ticketing – you buy your ticket by walking onto a train. Or it may be ordering food and drink to your location.

There’s still huge scope for big data and location intelligence to be applied to improve the customer experience.



Mobile device data as we have seen has connected many digital walks of life to offline consumer behaviour. Another way that this technology is revolutionising the marketing and advertising space is through attribution.

Traditionally many advertisers have been blind when it came to measuring the impact of offline ads on offline KPIs.

But the mobile device location data is filling in the blanks. Location intelligence can understand when a person is in front of, for example, OOH advertising. It can then measure how many of these people are then seen inside a store or in front of a specific physical product.

Connecting the two provides an accurate way for marketers to measure the impact and ROI of offline advertising inventory. it also allows them to measure the effect of digital advertising on an offline goal. These things have just not been possible with certainty before. But big data has changed the way that advertising can be measured.



If AR is really going to live up to its promises, it will have to rely on complex data sets and accurate location intelligence.

As AR gains prominence it’s application will move beyond fun to play games to useful productivity applications (you can even combine it with some powerful notion templates to really level up). As AR develops, it’s used as a way of reaching audiences with content and advertising will grow. Like previous marketing activity, it will be improved by the use of big data and location intelligence.

AR will require huge amounts of accurate and real-time location data to function properly as a user moves around the real world.

Optimising the supply chain

Big data and location intelligence is impacting organisations that want to optimise the supply chain.

The obvious application of location intelligence in the retail supply chain lies in the ability to understand and track deliveries and supplies. It already being used to generate data sets which can optimise and improve these services.

But location intelligence isn’t just helping business to optimise the process. It’s helping to understand the demand for products. There’s a lot of history of people building something in the hope that people want it to then find out that actually, they don’t.

Another way that big data is helping manufacturing industry to optimise is by helping it to adjust the type of transportation, pickup location or place of sale.

With the rise of location data, these insights are now fuelled by information from the offline world. Insights that have previously been unattainable or have lagged are now available in real-time. This lies at the heart of what is disrupting how the supply chain operates.


Privacy and transparency

As usual with new disrupting activity, the focus in on the responsibility of these new technologies. And rightly so. Indeed those in the big data space will need to be more transparent in how data sets are sourced.

It won’t be enough to simply check a box and start collecting and aggregating personal data. More needs to be done in order to clean up the data supply chain. More control needs to be handed to the user.

In this way, it’s our responsibility to communicate the value of big data and location intelligence to the user. It’s having a huge positive impact across the globe, and that’s just more reason to get the privacy part right.

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Learn more about big data

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Marketing & Advertising

Mobile Targeting – Get Programmatic & Social Right With Data

There have been issues with data accuracy in mobile targeting in the past. Targeting the right person in the best moment is still the appropriate goal for marketers. But to do this effectively, the data that fuels campaigns must be reliable.

If marketers don’t work from reliable data sets then the information is useless, and mobile targeting will be more of the same.

Today, the availability of scalable first-party data sets is there. Brands and advertisers need to be able to understand what good data should look like.

We’ll look at a few fundamentals to look for that will ensure your mobile targeting campaigns are powered by quality data. We’ll also discuss the effect this will have on different mobile targeting channels.


What is quality location data?

When we talk about data we look for the following attributes:

  • First-party – is the data from a first party source. Second-hand data that is unverifiable is not helpful as it could be inaccurate or out of date.
  • Sensor-driven – this means that the data sets are sourced from accurate sensors. Precise and reliable data sets are sourced from multiple sensor types to ensure accuracy and scale.
  • Real-time – Datasets must be immediate in order to verify accuracy. To achieve effective personalisation and mobile targeting, action must be taken based on data sets that are real-time.


Programmatic advertising & location data

Location is a fantastic trigger to help fuel mobile marketing campaigns. That is if you can get the moment right.

Programmatic has always had its problems – automation is difficult to get right. A lot has been said about programmatic and it’s effect on delivering relevant content to the right person at the right time.

We’ve all seen and remember poor individual use cases of programmatic advertising. Mobile programmatic targeting has taken the plunge and aims to put data at the centre.

However, problems of accuracy remain if the data that is being used to fuel programmatic advertising isn’t accurate.

Location-based marketing and programmatic are effective when the following conditions are met:

  • Mobile targeting can be achieved in real-time
  • Data is derived from accurate, sensor-driven networks
  • The data is first-party

Without this programmatic mobile targeting will be ineffective. Outdated data sets can mean that you completely miss the relevant moment to target audiences.

It can also mean that your attempts to personalize the experience miss the mark. We all know how important personalization is to the modern marketer.

There are a lot of companies that claim to provide accurate data, but these are rarely meeting the three conditions we just discussed.

Often the data is third-party, it’s driven not by accurate sensors, but vague lat/long indicators. It’s often not live or real-time either.

So make sure your data partner can deliver on those three points. This will allow your programmatic mobile targeting to truly feel personal. It’s also important to get a good understanding of what stream processing is.


Location-based social media marketing

With organic social media becoming more obsolete, more brands are looking at increasing their ad spend on social to ensure that they reach audiences constantly. One way to do so is by using social media management tools that automate posting regularly on all social channels for maximum reach.

Social targeting options allow for geographic targeting.  But the accuracy of these targeting options is yet to be verified. How do you know you aren’t targeting a user who checked in there over a week ago?

Let’s analyse current social media mobile targeting options in relation to our three commandments.

Real-time – Facebook’s geographical targeting feature is rather vague when talking about its geo-targeting options. Or at least when talking about the speed of the targeting. “people recently in this location” is how they describe it. But there’s little in the way of how recent.

Now, this is useful for some advertisers, but to create a truly personalized and real-time experience, it has to be instant.

Accurate sensor-driven – Again it’s hard to tell exactly how Facebook sources its location data. We suspect that a large proportion of data is derived from check-ins on the Facebook platform. This does raise some issues – it relies on the user selecting the right location, for example.

Social is a powerful channel for targeting users. But the potential is even greater if brands can accurately target users in real-time. It’s even more effective if this is done in relevant locations and with personalized content. This can even be leveraged to create social proof.

In order to achieve that, the data that fuels social targeting and retargeting needs to be accurate.

Social platforms have always focused on personalization of the news feed. But this highlights some of the problems with facebook ads – they aren’t relevant.

Facebook has spent so much time personalizing the organic news feed but then delivers any ad at any time.

This means that brands and advertisers who embark on Instagram marketing can ensure that relevant, in the moment ads, reach users using Instagram stories data will be on to a winning formula.


Post mobile targeting attribution

The same can be applied to attribution. Post mobile targeting attribution is valuable for advertisers and marketers. It’s important to measure attribution, especially in the offline world. Mobile location data has been instrumental in this. Closing the online to offline attribution loop is now possible thanks to device location data.

But again, to truly understand physical conversions, marketers need accurate and real-time data sets.



Mobile targeting is a powerful tool for marketers to reach users with personalised messages, at the right moment. Location data and location intelligence helps provide the context that mobile targeting takes place.

Whether programmatic or social, mobile targeting requires data. This data must be accurate, real-time and first-party to ensure that location-based mobile marketing is effective.

Precise data is now available at scale. This means that marketers now have a powerful tool at their disposal, as long as they utilize the right data sets.

Marketing & Advertising

The Evolution Of Location Based Marketing & Advertising

Let’s get stuck in with a definition.

Location-based marketing is the practice of using physical location to inform and optimize advertising, communication, targeting, loyalty and attribution.

This sometimes also known as location-based advertising or proximity marketing. At the most basic level, it means creating a one-to-one relationship with the customer. Emphasis is placed on communication in the right place, at the best time and with the relevant message.


A history of location-based marketing

Despite popular opinion, location-based advertising has been around for a while. Sure, it hasn’t always been backed by the smart technology in your mobile phone. Buy it has existed in some form. Local marketing strategies have been a key part of marketing since the practice began.

It might not have been as effective, but brands have been trying to target users based on location since well before most of you reading this were born. Advertising space has always been purchased based on its location. Be it a metro station in an affluent Paris Arrondissement. Or a teenager holding a sign advertising bagels in a certain street in NYC.

Location-based marketing has developed a lot since then. The underlying technology has advanced at an alarming rate. The ability to understand where audiences go and the ability to market to these is improved.


IP addresses and targeting over Wi-Fi

Location-based marketing has always been around. But, it did up its game in the 90’s once the internet found its way into most family homes.

The dial-up broadband revolution had begun. Most families now had internet access in their home through (a large) desktop computer. For marketers, this meant that advertising could be delivered to a user based on their IP address. Thus marketers could now target based on location, usually an area the size of a postal area.

There are a few problems though. Unless you have a very specific audience in a single geographical area, it’s difficult to ensure that you are targeting the right people. By today’s marketing standards, an entire postal area is just not specific enough.

Another problem that remains is the lack of ability to understand who is on the other end of the screen. When IP targeting was becoming popular, many families had a single computer. The whole family used this. Dad to research which TV to buy next. Mum to book the family holiday. Kids to play online games. This means that ad dollars could easily be wasted. As advertisers were under the impression that they were delivering highly targeted ads.


The beginnings of mobile targeting

Location-based mobile advertising was the next advance in location tech. Phones become truly mobile in the early 2000’s. They were used by the masses and even your gran had one. This meant two things:

It was now possible to advertise to an individual based on physical location whilst they were on the move.

Location-based marketing companies now had a means of segmentation down to a single person.

This was fantastic and all, but the main issues were still accuracy. Whilst the ability to target an individual on the move was well received, issues still remained. The area in which location could be accurately placed was still too large. Mobile targeting via phone masts was a vital step on the road to personalization. However, those in pursuit of accuracy were still frustrated. It often requires a form of phone validation to ensure the right person is reached.


GPS, geofencing and geotargeting

In the late 2000’s smartphones really took off. The first smart devices appeared and with the first iPhone, the race to create powerful mobile devices went mainstream. This led to rapid growth of mobile device adoption with GPS capability.

This changed mobile targeting for good. It was now possible to use a device’s precise GPS satellite positioning to understand device location. This process is known as geofencing, geomarketing, or geotargeting.

A geofence is a virtual boundary that is defined in order to perform a specific response once a device enters or leaves the defined area. More advanced geofencing is possible. An example is focusing on dwell time. Triggering a response when a device is within a geofence for a minimum amount of time.

The geofence allows for mobile targeting to occur on an individual level anywhere. It means that audience segmentation can occur based on individual movement. But it did more than previously possible. It was now far more accurate. This meant that personalization (relevant to location) was now available. Location-based marketing was now personal and precise.


Satellite problems and indoor confusion.

Location-based mobile targeting improved in accuracy with the geofence. However, on a precision basis, it’s still not perfect.

GPS has issues in some indoor spaces. So it might be incredibly effective at understanding where a person is in their car whilst driving. As soon as they enter an indoor space, GPS can be temperamental.

Some may argue that this level of detail is neither here nor there. But the problem arises when location data providers can’t differentiate between accurate data and inaccurate data. Advertisers and marketers don’t know when the data fuelling their campaigns is incorrect.


Beacons – iBeacon and Eddystone

You thought that it was location-based marketing you were doing before? How very wrong you were. Beacon adoption was a trend that changed location-based marketing and advertising even further.

Thanks to mobile devices and mobile targeting, advertisers would now focus all efforts on accuracy.

Beacons were the natural next step in this journey. Beacons are small Bluetooth devices that can interact with a mobile device. This interaction allows the device to understand exactly where the mobile device is with an accuracy down to 1 meter. Not only does it do this, it can measure positioning on a vertical basis.

There are two main types of beacon technology; iBeacon and Eddystone. These devices are deployed everywhere from shopping malls to sports stadiums.

This was the true beginning of proximity marketing. Delivering personalizable content with accuracy in precise micro-moments. These moments are relevant to the time, place and person. Beacons also allowed for accurate attribution in the offline world.


But what about scalability?

The problem with Beacons is that they require hardware to be deployed. Unlike Geofencing, where geofences can be set around any place that satellites can reach. Beacons must be physically deployed inside a store, or inside a football stadium. So these individual businesses can enjoy a high level of location-based marketing. Once a person leaves these areas they cannot get the same level of location-based insights.


Enter the network approach

The solution to this problem? The proximity network approach. To maintain beacon based, accurate levels across entire cities, it’s essential to collate this location based hardware. That’s what Tamoco’s proximity network is. It’s a complex network of location-based sensors. This allows for mobile targeting, location-based, marketing and location intelligence and insights at a consistently high level. Moreover, this is available whether the device is indoors, outside, on the first floor and so on.

One issue that many proximity marketing companies have had with location-based advertising is with scalability. Creating a proximity network of location allows for this scalability.


Sensory agnostic and a view of the future?

At Tamoco we take a sensory agnostic approach to location-based marketing. We believe that as technology advances, we need to able to adapt to the changing was that advertising, targeting and marketing will change.

A good example of this is the emerging tech around AR. An AR headset or device is still a sensor. Location is a means to an end for targeting through this medium.

By taking an agnostic approach and ensuring our network approach, we ensure that advertising and marketing can keep up with the cutting edge trends. Moreover, we can ensure that this is available with a precision and scale that those original metro advertisers could only dream of. Using location to improve marketing is here to stay.

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App Monetization Is About The App Experience – Here’s How

Is there a point where too much monetization can have a negative effect on the app experience? It’s more important than ever that mobile app developers understand the effect of app monetization.

Across app monetization strategies there are some mistakes that can have a negative effect on user experience. In these cases, it will cause you to lose valuable users. 

Are in-app ads damaging the app experience?

It’s amazing that so many developers fail to see this. Poorly implemented in-app advertising is one of the worst forms of mobile app monetization. It might be an obvious thing to say – but users don’t like ads. An app monetization strategy should be carefully managed and well devised. A mobile app monetization strategy that consists of ads can be successful. But when the ads are poorly implemented, they start to have a negative effect on user experience.

Protecting the user experience means taking care and understanding the effect of in-app ads. Too many intrusive and you’ll begin to lose those users that you’ve spent valuable time and money acquiring.

Typically the best ad formats for in-app monetization are native, interstitial and incentivized advertising. It’s important that the ad feels like it was designed to be in the app, not forced in at every opportunity. It’s also important that you think through where your ads appear. Do you have a clear idea of the best user experience? Ensure that your ads don’t affect this flow. Failure to understand this will lead to a negative user experience, and cause you to lose users.

App monetization is about striking the right balance between revenue generating strategies and improving the user experience. In terms of ads, content is often overlooked. Your ad content should be relevant to the user. There are many tools to ensure this, but one way to do this is to engage with affiliate sponsors. Striking a more bespoke advertisement agreement will allow you to choose the ad content. Affiliate ads on mobile generally take the form of another app. This ensures that the content is relevant. You can even offer affiliate advertisers an ad spot for one in return.


This app monetization strategy has become very popular withe the decrease in paid apps. With freemium, in-app monetization is about building a large base of users. Make sit clear up front that your users will only be able to access certain features without paying. Failure to do so will lead to some unpleasant app experiences.

Be transparent – it will be helpful in the long run. With this app business model, you’ll see that a small number of users will contribute a huge amount of revenue. In games, this type of user is generally someone who wants to advance throughout the game faster. Or unlock features that usually would take a regular user a significant amount of time. It is therefore important that you continue to generate and maintain the users that don’t pay anything. These free users are important as without they there would be no reason for paid users to continue paying for extras.

You must strike the right balance – between free features and paid features. If you get it wrong you’ll lose users. But of course, you also need to entice users to upgrade

In terms of experience, you can try educating the user better. Helpful, intuitive, experience first monetization is the solution. Also, don’t let your app become the next news story about a child spending millions on added content. We don’t need to tell you that’s not a positive app experience.


Subscription model

Another model that relies on experience first app monetization is the subscription model. By placing the experience first, app owners will produce better user retention and engagement. It’s simple math to understand that the more users on your app, the more that will enter into a paid subscription.

But placing experience first will also allow you to increase the percentage of users that enter into a subscription. Focus on creating improved UI and increasing user satisfaction. This will increase the number of users that subscribe to your service.

Don’t over confuse your options – users will become tired and move on. With more app subscriptions resembling SaaS services, make sure that your strategy cuts through the noise. Focus on an attractive user experience to maximize upgrades. Focus on simplicity when explaining the benefits of subscribing.

Rather than only focusing on converting new users into subscribers, remember to listen to your current subscribers. What are their complaints? What are the features they want? Many developers think (incorrectly) that once they have a paid subscriber they have one for life. In truth app experience is just as important after the moment of subscription as it is before.

Data monetization

Most mobile app monetization strategies will have some kind of negative effect on the app experience. The exception to this is data monetization. By running in the background, app developers can generate large CPMs from their audience without having a negative effect on the user experience.

This opens up a wider debate on the nature of app monetization. It’s important that users realise the tradeoff between experience and revenue model. When using this app monetization strategy developers must recognise user privacy. They must also be able to communicate why apps are free. It’s important that developers and users engage in debate around the benefits of free apps. Users must also understand the reasons for this.

Other in-app monetization advice

To protect the user experience and maximise app monetization make sure that you don’t make any of the following mistakes.

Make sure that your app monetization strategy works across platforms. If your app exists on multiple mobile platforms then make sure that your strategy is adapted to each. This could be as simple as optimizing ad formats on different screen sizes. It could mean that you’ll need to utilize a completely different app monetization model. The main rule is to understand the audience across platforms. Then adapt your app monetization strategy accordingly.

Use analytics. One of the most important things that developers can incorporate into app monetization is data. Use data generated from monetization to understand your progress. There are plenty of tools to help developers understand app engagement and app retention. Combine this with an app monetization platform that can give you accurate insights on how app engagement can affect revenue. Connecting the two is key to succeeding at in-app monetization.

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Marketing & Advertising

Cross-Device Attribution, Location & The Customer Journey

Marketing attribution has always been a tough area for marketers and advertisers. Attribution modelling has undoubtedly provided huge value. However, the ability to measure the effect of channels (or touchpoints) on the customer journey has often been fraught with difficulties.

Return on investment has been difficult for a variety of reasons. Understanding the entire customer journey has been problematic. This is especially true in the physical, offline world. Some offline marketing channels have also not been trackable. Attribution solutions have struggled with the multi-channel and real-time aspects that are crucial to understanding the full marketing picture.

Location intelligence has grown in terms of accuracy and scalability. This presents an opportunity. Some of the problems with attribution modelling can be solved with the application of accurate location data. Could this be the solution to the problems that limited marketing attribution in the past?


What is marketing attribution?

To understand the problems and the effect that location data can have it’s important to understand marketing attribution.

Attribution is the practice of allocating purchase revenue to the marketing touch points of a customer. In other worlds – understanding the effect of marketing efforts and channels on the purchase decision of customers.

Touchpoints can cover a wide range of customer interactions. Understanding the effect of these on sales or other valuable metrics allows for the optimisation of marketing channels, activity and budget.


What is offline location-based attribution?

Location-based attribution is the use of accurate mobile device data to fill in the gaps in traditional attribution models.

Smartphone adoption has grown rapidly. Understanding the where and how people move becomes scalable and precise. Customers rarely move without their mobile device, and this is the key. Higher levels of attribution precision are possible. Connecting the online and offline worlds becomes easier. Customer journey mapping and various touch point measurement is improved.

Until recently it has been impossible to understand the offline world. This has meant that advertisers have often been unable to attribute sales in physical stores and locations to a specific channel.

As smartphone adoption has grown using a device location has proved extremely useful in connecting the two.

Mapping the customer journey – cross device attribution

Basic attribution models have chosen to measure first touch or last touch. Much has been written about the failings of each. The choice lies in ignoring either early, top of funnel activity. Or failing to consider later, bottom of funnel activity that helps to move the customer along the buyer journey.

So the natural next step is to focus on multi-touch attribution. Focusing on touchpoints throughout the customer journey is important. But it requires accurate measurement across channels to be effective. The problem is that multi-touch attribution models don’t always incorporate what is happening in the offline world.

Location data allows a complete understanding of the customer journey. This means that it becomes possible for businesses to say the sort of thing like – okay this person saw our Facebook ad and has now completed a purchase. Previous attribution models would then attribute this purchase to the Facebook ad and not demonstrate how to generate leads on Facebook. But a more holistic view of the individual customer might point out that actually, the customer had visited the physical store previously.

This ability to model attribution across the online and the offline leads to a clearer picture of attribution. It allows brands to be better informed about the effect digital has on physical and vice-versa. Your customers exist across multiple marketing channels, so your attribution should too.

Previously brands have tried to close this gap by using various methods to map the offline customer journey. This usually took the form of a promotional code, which allows the brand to understand which channel had the desired effect. But whilst the picture is slightly clearer, it is not enough to be able to inform marketing budgets. Or to provide a clear understanding of the customer journey and the customer experience.

Only location intelligence can provide these insights. And it can do this across the online and offline world with a sufficient level of detail. Location data is versatile, quick and accurate. This makes it the perfect tool to help close the offline to online attribution loop.

Location data connect online advertising to the offline world. This allows for attribution in physical locations. This allows brands to measure store visits and link this offline activity to other digital touch points. It allows for more accurate customer journey mapping. This data can even be used to understand external offline touch points, such as OOH advertising. Already a complete picture becomes available.

Attribution has always had its problems. But brands and marketers should understand and implement insights from customer data points. In this way, location data provides a better understanding of the offline world. It allows brands to measure touch points more accurately. It allows them to map the customer journey in greater detail. And most of all, it allows them to measure the effects of cross-channel marketing in detail. 

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Discover location based marketing

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